# CompTIA Data+ Certification Training Course Canonical URL: ## Overview In this course, you will cover the knowledge and skills required to transform business requirements in support of data-driven decisions by mining data, manipulating data, applying basic statistical methods, and analyzing complex data sets while adhering to governance and quality standards throughout the entire data lifecycle. In addition, it will help prepare candidates to take the CompTIA Data+ certification exam. ## What you'll learn - Mine data. - Manipulate data. - Visualize and report data. - Apply basic statistical methods. - Analyze complex datasets while adhering to governance and quality standards throughout the entire data life cycle. ## Prerequisites Students should have experience with data tools such as SQL, Excel, and Python. ## Curriculum #### Lesson 1: Identifying Basic Concepts of Data Schemas - Understand the fundamental concepts of data schemas. - Learn how data schemas are structured and used in business environments. - Explore the role of schemas in data storage and organization. #### Lesson 2: Understanding Different Data Systems - Learn about various data systems and their purposes. - Compare and contrast relational and non-relational data systems. - Understand the advantages and challenges of each system type. #### Lesson 3: Understanding Types and Characteristics of Data - Explore different data types (e.g., structured, semi-structured, unstructured). - Understand the characteristics of data and how they impact analysis. - Examine data quality and its importance in decision-making. #### Lesson 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages - Learn about common data structures and formats used in analytics. - Understand how markup languages influence data representation. - Explore how these structures impact data integration and analysis. #### Lesson 5: Explaining Data Integration and Collection Methods - Learn the methods used for collecting and integrating data from various sources. - Understand best practices for ensuring accurate data collection. - Explore tools and techniques used in data integration. #### Lesson 6: Identifying Common Reasons for Cleansing and Profiling Data - Understand the importance of data cleansing and profiling. - Learn techniques for identifying data quality issues. - Examine strategies to improve data integrity. #### Lesson 7: Executing Different Data Manipulation Techniques - Explore data manipulation techniques such as filtering, sorting, and transforming data. - Learn how to apply these techniques to clean and optimize data. #### Lesson 8: Explaining Common Techniques for Data Manipulation and Optimization - Understand optimization strategies for handling large datasets. - Learn about the role of indexing and query optimization in data systems. #### Lesson 9: Applying Descriptive Statistical Methods - Learn how to apply descriptive statistics such as mean, median, and mode. - Understand the importance of these methods in data analysis. #### Lesson 10: Describing Key Analysis Techniques - Examine key analysis techniques such as regression and correlation. - Understand how these techniques help in drawing business conclusions. #### Lesson 11: Understanding the Use of Different Statistical Methods - Learn about various statistical methods used in data analysis. - Explore the application of these methods in solving real-world business problems. #### Lesson 12: Using the Appropriate Type of Visualization - Learn how to select and use the correct type of visualization for different data sets. - Understand the principles of data visualization design. #### Lesson 13: Expressing Business Requirements in a Report Format - Learn how to translate business requirements into structured data reports. - Understand how to present data in an easily understandable format for stakeholders. #### Lesson 14: Designing Components for Reports and Dashboards - Learn how to design effective components for reports and dashboards. - Understand best practices in report design for data-driven decisions. #### Lesson 15: Distinguishing Different Report Types - Understand the differences between operational, analytical, and strategic reports. - Learn how to choose the right report type based on business needs. #### Lesson 16: Summarizing the Importance of Data Governance - Understand the concept of data governance and its impact on data management. - Learn about policies, procedures, and standards for managing data governance. #### Lesson 17: Applying Quality Control to Data - Learn quality control techniques and their application in data management. - Understand how to maintain data quality throughout the lifecycle. #### Lesson 18: Explaining Master Data Management Concepts - Learn about master data management and its importance in managing critical business data. - Understand how master data management ensures consistency and accuracy across systems. ## Instructors ### Bruce Gay — Instructor Bruce is an engaging trainers and program manager who brings 25+ years practical experience to deliver effective and experiential training to students. Able to engage adult learners with a range of backgrounds and professional experiences. Successful at building effective stakeholder relationships and coordinating multi-disciplinary teams for solution delivery. Bruce has over 25 years of project and program management experience across multiple industries. He has a Masters degree from The George Washington University and a B.A. from the University of North Carolina Chapel Hill. Bruce currently runs his own freelance training and consulting business, helping project managers and team leaders improve their business skills, become better leaders, and achieve professional greatness. Bruce is a well-received speaker in the areas of design thinking, project management, cross-team collaboration, and AI tools for projects, and has presented at regional and international conferences. ### Steve Pesklo — Instructor Steve is an energetic trainer who focuses on applying technical concepts to everyday work practices. He is the founder and president of SoftLake Solutions, a company that specializes in providing data and AI applications to identify fraud for Internal Audit, Criminal Investigations, Forensic Accounting, Privacy, and Compliance. Steve brings a large amount of experience across multiple industries and government agencies. He is an expert in implementing large data analysis projects across the world, including Inland Revenue in the UK and Argentina, New Zealand, Africa and across Europe. Previously, he was the manager of Data Architecture and Data Services for a large mortgage company. He is a frequent speaker on data analytics and project management topics and speaks fluent German. He has been teaching at the Graduate School for over 10 years. Steve has an M.B.A. from the University of St. Thomas and a B.S. in Computer Science from California Lutheran University and the Universität Salzburg in Austria. He is certified as a Certified Fraud Examiner (CFE), Project Management Professional (PMP), and a Certified ScrumMaster (CSM). ### Joe Mlakar — Instructor Joe has over 27 years of Federal Government and military service and has been a part-time instructor with Graduate School USA since 2023. He enjoys using his technical knowledge in Operations Research to teach his students to provide organization and structure to complex processes, and apply advanced analytical techniques to help leaders make better decisions. Joe is based in Fort Collins, Colorado. ## Pricing **Tuition:** $2499